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Institute of Automation, Chinese Academy of Sciences
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Coding agents falter in collaborative settings, with a 7.7% drop in task resolution when users modify code, revealing a crucial gap in their adaptability.
LLMs can be made to reason much better by directly optimizing their pre-training output distribution, even before fine-tuning on specific tasks.
